Versatile generation of accurate 3D molecular models

The Advantages of Accurate 3D Ligand Libraries

Computational methods have become an indispensable part of lead identification efforts. Nearly all methods require accurate 3D molecular models as a starting point. However, many corporate and purchasable compound databases contain only 2D molecular structures. Efficient and accurate 2D to 3D conversion is therefore a key precursor to computational analyses.

Beyond simple one-to-one structural conversion, it is equally important to generate scientifically sound molecular models that enumerate the different structural and chemical possibilities a ligand could sample, as these variations could lead to dramatically different results in subsequent computations. A versatile conversion program that can be configured to generate ligand libraries with the desired structural and chemical features can significantly streamline the entire in silico drug discovery process. 

Chemically correct models:
LigPrep generates accurate, energy minimized 3D molecular structures. LigPrep also applies sophisticated rules to correct Lewis structures and to eliminate mistakes in ligands in order to reduce downstream computational errors.

Maximum diversity:
LigPrep optionally expands tautomeric and ionization states, ring conformations, and stereoisomers to produce broad chemical and structural diversity from a single input structure.

Customized libraries:
LigPrep applies filters to eliminate compounds that do not meet user-specified criteria, allowing the generation of a completely customized ligand library.

Preprocessing for Schrödinger simulations:
LigPrep has settings specially tuned for generating input structures for Glide and Phase. Using these settings will optimize the output structures to meet the requirements of the simulation programs without necessitating any further user intervention.

Efficient conversion:
LigPrep processes approximately one ligand per second, making it possible to convert entire databases at one time.

Citations and Acknowledgements

Schrödinger Release 2021-4: LigPrep, Schrödinger, LLC, New York, NY, 2021.

"Toward in vivo-relevant hERG safety assessment and mitigation strategies based on relationships between non-equilibrium blocker binding, three-dimensional channel-blocker interactions, dynamic occupancy, dynamic exposure, and cellular arrhythmia"

Wan, H.; Selvaggio, G.; Pearlstein, R.A., BioRxiv, 2020, Preprint, XXX-XXX

"Novel, Self-Assembling Dimeric Inhibitors of Human β Tryptase"

Giardina, S.F.; Werner, D.S.; Pingle, M.; Feinberg, P.B.; Foreman, K.W.; Bergstrom, D.E.; Arnold, L.D.; Barany, F., J. Med. Chem., 2020, 63(6), 3004–3027

"Toward the Rational Design of Sustainable Hair Dyes Using Cheminformatics Approaches: Step 2. Identification of Hair Dye Substance Database Analogs in the Max Weaver Dye Library"

Williams, T.N., Van Den Driessche, G.A., Valery, A.R.B., Fourches, D., Freeman, H.S., ACS Sustainable Chem. Eng., 2018, DOI: 10.1021/acssuschemeng.8b02882,

ö "High throughput evaluation of macrocyclization strategies for conformer stabilization"

Sindhikara, D. and Borrelli, K., Nature, Scientific Reports , 2018, 8 (6585), doi:10.1038/s41598-018-24766-5

"In silico Predicted Glucose-1-phosphate Uridylyltransferase (GalU) Inhibitors Block a Key Pathway Required for Listeria Virulence"

Kuenemann, M.A.; Spears, P.A.; Orndorff, P.E., Fourches, D., Mol. Inf., 2018, 37, 1800004

"Adverse Drug Reactions Triggered by the Common HLA-B*57:01 Variant: A Molecular Docking Study"

Van Den Driessche, G.; Fourches, D., J. Cheminform., 2017, 9 (13), 1-17

"Characterizing the Chemical Space of ERK2 Kinase Inhibitors Using Descriptors Computed from Molecular Dynamics Trajectories"

Ash, J.; Fourches, D., J. Chem. Inf. Model., 2017, 57 (6), 1286–1299

ö "Relative Binding Free Energy Calculations Applied to Protein Homology Models"

Cappel, D.; Hall, M.L.; Lenselink, E.B.; Beuming, T.; Qi, J.; Bradner, J.; Sherman, W., J. Chem. Inf. Model., 2016, 56 (12), 2388–2400

ö "AutoQSAR: An Automated Machine Learning Tool for Best-Practice QSAR Modeling"

Dixon, S.L.; Duan, J.; Smith, E.; Von Bargen, C.D.; Sherman, W.; Repasky, M.P., Future Med. Chem., 2016, 8 (15), 1825-1839

"Discovery of Thienoquinolone Derivatives as Selective and ATP Non-Competitive CDK5/p25 Inhibitors by Structure-Based Virtual Screening"

Chatterjee, A.; Cutler, S.J.; Doerksen, R.J.; Khan, I.A.; Williamson, J.S., Bioorg. Med. Chem., 2014, 22, 6409-6421

"The marine-derived sipholenol A-4-O-3′,4′-dichlorobenzoate inhibits breast cancer growth and motility in vitro and in vivo through the suppression of Brk and FAK signaling"

Akl, M.R.; Foudah, A.I.; Ebrahim, H.Y.; Meyer, S.A.; El Sayed, K.A., Mar. Drugs, 2014, 12(4), 2282-2304

"Optimization, Pharmacophore Modeling and 3D-QSAR Studies of Sipholanes as Breast Cancer Migration and Proliferation Inhibitors"

Foudah, A.I.; Sallam, A.A.; Akl, M.R.; El Sayed, K.A., Eur. J. Med. Chem., 2014, 73, 310-324

ö "Boosting virtual screening enrichments with data fusion: Coalescing hits from two-dimensional fingerprints, shape, and docking"

Sastry, G.M.; Inakollu, V.S.; Sherman, W, J. Chem. Inf. Model., 2013, 53, 1531-1542

"Computational Validation of the Importance of Absolute Stereochemistry in Virtual Screening"

Brooks, W.; Daniel, K.; Sung, S.; Guida, W., J. Chem. Inf. Model, 2008, 48, 639-645

"Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance to tool selection?"

Good, A.C; Oprea, T. I., J. Comput. Aided Mol. Des., 2008, 22, 169-178

"Pose prediction accuracy in docking studies and enrichment of actives in the active site of GSK-3β"

Gadakar, P.K.; Phukan, S.; Dattatreya, P.; Balaji, V.N., J. Chem. Inf. Model., 2007, 47, 1446-1459

"Novel Human Lipoxygenase Inhibitors Discovered Using Virtual Screening with Homology Models"

Kenyon, V.; Chorny, I.; Carvajal, W.; Holman, T., Jacobson, M., J. Med. Chem., 2006, 49, 1356-1363
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